The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

Self-training methods have gained significant attention in recent years due to their
effectiveness in leveraging small labeled datasets and large unlabeled observations for …

Labeling training data has become one of the major roadblocks to using machine learning.
Among various weak supervision paradigms, programmatic weak supervision (PWS) has …

Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …

Strong student models can learn from weaker teachers: when trained on the predictions of a
weaker model, a strong pretrained student can learn to correct the weak model's errors and …

Neural sequence labeling is widely adopted for many Natural Language Processing (NLP)
tasks, such as Named Entity Recognition (NER) and slot tagging for dialog systems and …

We propose a new strategy for applying large pre-trained language models to novel tasks
when labeled training data is limited. Rather than apply the model in a typical zero-shot or …

Labeling training data is a critical and expensive step in producing high accuracy ML
models, whether training from scratch or fine-tuning. To make labeling more efficient, two …

Among the biggest challenges we face in utilizing neural networks trained on waveform (ie,
seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement …

Existing weak supervision approaches use all the data covered by weak signals to train a
classifier. We show both theoretically and empirically that this is not always optimal …